Add moscot #16
Add moscot #16
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| assert "centroid_x" in adata_sp.obs and "centroid_y" in adata_sp.obs, '"Observation level columns "centroid_x" and "centroid_y" are required for spatial anndata' | ||
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| # Use normalized layer and create spatial obsm | ||
| adata_sc.X = adata_sc.layers["normalized"] |
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Normalized data or raw counts?
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As stated in Mapping Gene Expression Space tutorial from the Moscot documentation, we should use normalized data:
Usually, it’s the X attribute, which contains normalized counts...
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| # TODO: check parameters: sc_attr, xy_callback, x_callback, y_callback | ||
| mp = mp.prepare( | ||
| sc_attr={"attr": "obsm", "key": "X_pca"}, |
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Use X_pca, X_umap or adata.X?
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The Mapping Gene Expression in Space tutorial gives us to options:
- directly using normalized counts by passing
sc_attr={"attr": "layers", "key": "normalized"} - using the pre-computed PCA by passing
sc_attr={"attr": "obsm", "key": "X_pca"}
Usually, it’s the X attribute, which contains normalized counts, but a pre-computed PCA could also be used.
So we probably use the X attribute, but I run some tests using the pca as well to examine the differences in the results.
| # TODO: check parameters: sc_attr, xy_callback, x_callback, y_callback | ||
| mp = mp.prepare( | ||
| sc_attr={"attr": "obsm", "key": "X_pca"}, | ||
| xy_callback="local-pca", |
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Not sure what xy_callback does and how it compares the x_callback and y_callback.
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The Passing Callbacks in Prepare Example explain the use of those parameters:
The callback argument states which computation should be run on X to get the joint cost when preparing the problem. [...].
To create a joint PCA embedding between two sets of genes, we compute the PCA embedding for pairs of distributions by passing xy_callback="local-pca" to run on X.
Since it is also used in the Annotation Mapping Example, I would opt for using the local-pca callback.
initial moscot module, right parameter configuration TBD